Site Characteristics
| LENO |
18.1 |
1386 |
American Sweetgum, American Hornbeam, Possumhaw |
44.7 +- 13.8 |
1.0 +- 0.4 |
| DELA |
17.6 |
1372 |
Water oak, Red maple, Sugarberry |
37.0 +- 6.3 |
0.88 +- 0.2 |
| ORNL |
14.4 |
1340 |
Red maple, Sour gum, Chestnut oak |
22.0 +- 12.7 |
0.13 +- 0.05 |
| SERC |
13.6 |
1075 |
Tulip poplar, American Beech, American Sweetgum |
17.4 +- 6.2 |
0.27 +- 0.1 |
| HARV |
7.4 |
1199 |
Eastern Hemlock, Northern Red Oak, American
Beech |
5.5 +- 1.9 |
0.38 +- 0.1 |
| BART |
6.2 |
1325 |
American Beech, Eastern Hemlock, Red maple |
3.6 +- 0.7 |
0.76 +- 0.5 |
| TREE |
4.8 |
797 |
Sugar maple, Red maple, Gray alder |
5.4 +- 1.1 |
0.34 +- 0.09 |
|
|
proportion MAOM C
|
[MAOM C]
|
proportion MAOM N
|
[MAOM N]
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
std. p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
std. p
|
df
|
|
Intercept
|
0.80
|
0.04
|
-0.00
|
0.15
|
<0.001
|
40.00
|
29.67
|
7.52
|
-0.00
|
0.23
|
<0.001
|
40.00
|
0.88
|
0.04
|
0.03
|
0.12
|
<0.001
|
0.824
|
39.00
|
1.68
|
0.43
|
0.02
|
0.18
|
<0.001
|
0.900
|
39.00
|
|
Mycorrhizal dominance
|
-0.10
|
0.03
|
-0.42
|
0.13
|
0.003
|
40.00
|
-1.66
|
3.09
|
-0.06
|
0.11
|
0.596
|
40.00
|
-0.19
|
0.07
|
-0.23
|
0.12
|
0.011
|
0.059
|
39.00
|
-1.23
|
0.56
|
-0.06
|
0.11
|
0.034
|
0.588
|
39.00
|
|
CDI
|
0.17
|
1.24
|
0.02
|
0.17
|
0.893
|
40.00
|
-573.04
|
211.44
|
-0.67
|
0.25
|
0.010
|
40.00
|
0.73
|
1.17
|
0.50
|
0.14
|
0.539
|
0.001
|
39.00
|
-27.98
|
11.54
|
-0.24
|
0.20
|
0.020
|
0.224
|
39.00
|
|
FeOx
|
0.09
|
0.03
|
0.49
|
0.16
|
0.004
|
40.00
|
13.68
|
3.32
|
0.61
|
0.15
|
<0.001
|
40.00
|
0.01
|
0.02
|
0.09
|
0.14
|
0.521
|
0.521
|
39.00
|
1.00
|
0.20
|
0.77
|
0.15
|
<0.001
|
<0.001
|
39.00
|
Mycorrhizal dominance * CDI
|
|
|
|
|
|
|
|
|
|
|
|
|
4.34
|
2.02
|
0.24
|
0.11
|
0.038
|
0.038
|
39.00
|
32.82
|
15.62
|
0.21
|
0.10
|
0.042
|
0.042
|
39.00
|
|
Random Effects
|
|
σ2
|
0.00
|
37.45
|
0.00
|
0.13
|
|
τ00
|
0.00 Site
|
27.49 Site
|
0.00 Site
|
0.05 Site
|
|
ICC
|
0.09
|
0.42
|
|
0.27
|
|
N
|
7 Site
|
7 Site
|
7 Site
|
7 Site
|
|
Observations
|
46
|
46
|
46
|
46
|
|
Marginal R2 / Conditional R2
|
0.326 / 0.385
|
0.362 / 0.632
|
0.422 / NA
|
0.504 / 0.638
|
|
|
proportion.C_OLF
|
mg.C.per.g.soil_OLF
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
|
(Intercept)
|
0.066
|
0.031
|
0.004
|
0.181
|
0.038
|
40.000
|
3.499
|
1.347
|
0.004
|
0.189
|
0.013
|
40.000
|
|
E
|
0.047
|
0.021
|
0.329
|
0.148
|
0.032
|
40.000
|
1.282
|
0.818
|
0.208
|
0.133
|
0.125
|
40.000
|
|
CDI
|
0.442
|
0.857
|
0.105
|
0.203
|
0.609
|
40.000
|
-78.625
|
37.834
|
-0.432
|
0.208
|
0.044
|
40.000
|
|
FeOx
|
-0.019
|
0.020
|
-0.175
|
0.182
|
0.344
|
40.000
|
1.203
|
0.812
|
0.253
|
0.171
|
0.146
|
40.000
|
|
Random Effects
|
|
σ2
|
0.002
|
2.697
|
|
τ00
|
0.000 Site
|
0.558 Site
|
|
ICC
|
0.099
|
0.172
|
|
N
|
7 Site
|
7 Site
|
|
Observations
|
46
|
46
|
|
Marginal R2 / Conditional R2
|
0.099 / 0.188
|
0.215 / 0.350
|
|
|
proportion.N_OLF
|
mg.N.per.g.soil_OLF
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
std. p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
std. p
|
|
(Intercept)
|
0.032
|
0.026
|
-0.030
|
0.133
|
0.229
|
0.823
|
0.100
|
0.056
|
-0.006
|
0.193
|
0.082
|
0.975
|
|
E
|
0.108
|
0.043
|
0.187
|
0.139
|
0.017
|
0.187
|
0.068
|
0.080
|
0.094
|
0.136
|
0.405
|
0.493
|
|
CDI
|
0.099
|
0.693
|
-0.395
|
0.156
|
0.887
|
0.015
|
-1.997
|
1.506
|
-0.484
|
0.214
|
0.193
|
0.029
|
|
FeOx
|
0.006
|
0.012
|
0.084
|
0.161
|
0.607
|
0.607
|
0.049
|
0.027
|
0.333
|
0.185
|
0.080
|
0.080
|
|
E * CDI
|
-2.587
|
1.194
|
-0.281
|
0.130
|
0.036
|
0.036
|
-1.447
|
2.245
|
-0.083
|
0.129
|
0.523
|
0.523
|
|
Random Effects
|
|
σ2
|
0.001
|
0.003
|
|
τ00
|
0.000 Site
|
0.001 Site
|
|
ICC
|
|
0.168
|
|
N
|
7 Site
|
7 Site
|
|
Observations
|
46
|
46
|
|
Marginal R2 / Conditional R2
|
0.247 / NA
|
0.200 / 0.334
|
|
|
proportion.C_FLF
|
mg.C.per.g.soil_FLF
|
proportion.N_FLF
|
mg.N.per.g.soil_FLF
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
df
|
|
(Intercept)
|
0.136
|
0.030
|
0.000
|
0.136
|
<0.001
|
40.000
|
4.658
|
1.521
|
-0.004
|
0.217
|
0.004
|
40.000
|
0.118
|
0.021
|
-0.000
|
0.124
|
<0.001
|
40.000
|
0.172
|
0.051
|
-0.005
|
0.213
|
0.002
|
40.000
|
|
E
|
0.051
|
0.023
|
0.284
|
0.129
|
0.033
|
40.000
|
1.047
|
0.757
|
0.168
|
0.121
|
0.174
|
40.000
|
0.028
|
0.018
|
0.200
|
0.130
|
0.132
|
40.000
|
0.021
|
0.027
|
0.098
|
0.127
|
0.444
|
40.000
|
|
CDI
|
-0.691
|
0.833
|
-0.130
|
0.157
|
0.412
|
40.000
|
-86.039
|
42.830
|
-0.467
|
0.233
|
0.051
|
40.000
|
-1.597
|
0.587
|
-0.394
|
0.145
|
0.010
|
40.000
|
-3.009
|
1.448
|
-0.477
|
0.230
|
0.044
|
40.000
|
|
FeOx
|
-0.068
|
0.021
|
-0.492
|
0.151
|
0.002
|
40.000
|
-0.182
|
0.788
|
-0.038
|
0.164
|
0.819
|
40.000
|
-0.025
|
0.015
|
-0.235
|
0.146
|
0.115
|
40.000
|
0.002
|
0.028
|
0.013
|
0.169
|
0.940
|
40.000
|
|
Random Effects
|
|
σ2
|
0.002
|
2.266
|
0.001
|
0.003
|
|
τ00
|
0.000 Site
|
0.966 Site
|
0.000 Site
|
0.001 Site
|
|
ICC
|
0.037
|
0.299
|
0.000
|
0.263
|
|
N
|
7 Site
|
7 Site
|
7 Site
|
7 Site
|
|
Observations
|
46
|
46
|
46
|
46
|
|
Marginal R2 / Conditional R2
|
0.337 / 0.362
|
0.256 / 0.478
|
0.323 / 0.323
|
0.223 / 0.427
|
#what % of total C and N was MAOM C in each site?
pct.MAOM.C=final_data %>%
group_by(Site) %>%
mutate(mean.C.pro.maom=mean(proportion.C_HF),
se.pro.maom=sd(proportion.C_HF)/sqrt(46)) %>%
dplyr::select(plotID,mean.C.pro.maom, se.pro.maom)
## Adding missing grouping variables: `Site`
pct.MAOM.N=final_data %>%
group_by(Site) %>%
mutate(mean.N.pro.maom=mean(proportion.N_HF),
se.pro.maom.N=sd(proportion.N_HF)/sqrt(46)) %>%
dplyr::select(plotID,mean.N.pro.maom, se.pro.maom.N)
## Adding missing grouping variables: `Site`
# what % of total C and N was MAOM at the lowest %ECM and at the highest %ECM in each site?
end_members_maom_prop=final_data %>%
select(Site,Plot.x,E, proportion.C_HF, proportion.N_HF) %>%
arrange(Site,E) %>%
group_by(Site) %>%
mutate(myc_rank=order(E, decreasing=T)) %>%
mutate(max_A=max(myc_rank)) %>%
filter(myc_rank==max_A) %>%
ungroup() %>%
mutate(mean_prop_C=mean(proportion.C_HF),
mean_prop_N=mean(proportion.N_HF))
#At max ECM dominance, avg. proportion C in MAOM was 0.75831, N was 0.85565
#At max AM dominance, avg proportion C in MAOM was 0.83787, N was 0.89959
#from AM to ECM, MAOM C prop dropped 7.956%, N dropped 4.39%
ggarrange(propC.myc.flf, propC.myc.olf, propC.myc, nrow=1, ncol=3, common.legend=T, legend="right", labels=c("a", "b","c"))
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("som.C.proportion.plots.jpg",path="figures/", width= 30, height= 10, units="cm")
ggarrange(propN.myc.flf, propN.myc.olf, propN.myc, nrow=1, ncol=3, common.legend=T, legend="right", labels=c("a", "b","c"))
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("som.N.proportion.plots.jpg",path="figures/", width= 30, height= 10, units="cm")

concC.fe.hf=ggplot(final_data, aes(x=FeOx, y=mg.C.per.g.soil_HF, color=Site))+
geom_smooth(method="lm",size=2, se=F)+
geom_point()+
scale_colour_viridis_d()+
labs(x=" % FeOx ", y="[MAOM C] (mg/g soil)", colour="Site")+
theme_cowplot()+
theme(legend.title=element_text(hjust=0.5, size=16), legend.text=element_text(size=15), axis.text=element_text(size=15), axis.title=element_text(size=19))
propC.fe.hf=ggplot(final_data, aes(x=FeOx, y=proportion.C_HF, color=Site))+
geom_smooth(method="lm",size=2, se=F)+
geom_point()+
scale_colour_viridis_d()+
labs(x=" % FeOx ", y=expression("proportion C"[MAOM]), colour="Site")+
theme_cowplot()+
theme(legend.title=element_text(hjust=0.5, size=16), legend.text=element_text(size=15), axis.text=element_text(size=15), axis.title=element_text(size=19))
concN.fe.hf=ggplot(final_data, aes(x=FeOx, y=mg.N.per.g.soil_HF, color=Site))+
geom_smooth(method="lm",size=2, se=F)+
geom_point()+
scale_colour_viridis_d()+
labs(x=" % FeOx ", y="[MAOM N] (mg/g soil)", colour="Site")+
theme_cowplot()+
theme(legend.title=element_text(hjust=0.5, size=16), legend.text=element_text(size=15), axis.text=element_text(size=15), axis.title=element_text(size=19))
ggarrange( propC.fe.hf,concC.fe.hf, nrow=1, ncol=2, common.legend=T, legend="right")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("hf.conc.prop.fe.pdf",path="figures/", width= 20, height= 10, units="cm")
#what are the slopes of these lines?
fe_conchf_slopes_C=coef(lmList(mg.C.per.g.soil_HF~FeOx|Site , data = final_data))[2]
fe.mod1.slopes= as.data.frame(forests)%>%
arrange(forests) %>%
cbind(fe_conchf_slopes_C$FeOx) %>%
summarize(mean_slope=mean(fe_conchf_slopes_C$FeOx))
fe_conchf_slopes_N=coef(lmList(mg.N.per.g.soil_HF~FeOx|Site , data = final_data))[2]
fe.mod2.slopes= as.data.frame(forests)%>%
arrange(forests) %>%
cbind(fe_conchf_slopes_N$FeOx) %>%
summarize(mean_slope=mean(fe_conchf_slopes_N$FeOx))
|
|
d14C
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
|
(Intercept)
|
35.42
|
50.94
|
-0.03
|
0.41
|
0.492
|
|
FeOx
|
19.64
|
13.84
|
0.20
|
0.14
|
0.165
|
|
E
|
-10.03
|
13.03
|
-0.08
|
0.11
|
0.447
|
|
CDI
|
-1505.90
|
1460.04
|
-0.43
|
0.42
|
0.310
|
|
Random Effects
|
|
σ2
|
557.50
|
|
τ00 Site
|
1499.97
|
|
ICC
|
0.73
|
|
N Site
|
6
|
|
Observations
|
39
|
|
Marginal R2 / Conditional R2
|
0.082 / 0.751
|
|
|
d14C
|
d14C
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
|
(Intercept)
|
-22.76
|
17.96
|
-0.03
|
0.38
|
0.214
|
-30.57
|
50.43
|
-0.02
|
0.36
|
0.548
|
|
mg C per g soil HF
|
0.83
|
0.54
|
0.19
|
0.12
|
0.134
|
|
|
|
|
|
|
proportion C HF
|
|
|
|
|
|
27.71
|
59.64
|
0.05
|
0.11
|
0.645
|
|
Random Effects
|
|
σ2
|
539.88
|
582.81
|
|
τ00
|
1308.65 Site
|
1185.28 Site
|
|
ICC
|
0.71
|
0.67
|
|
N
|
6 Site
|
6 Site
|
|
Observations
|
39
|
39
|
|
Marginal R2 / Conditional R2
|
0.031 / 0.717
|
0.002 / 0.671
|
|
|
d14C
|
d14C
|
d14C
|
d14C
|
d14C
|
d14C
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
|
(Intercept)
|
-106.69
|
55.31
|
-0.00
|
0.46
|
0.149
|
-27.47
|
22.08
|
-0.00
|
0.43
|
0.281
|
43.26
|
26.93
|
-0.00
|
0.33
|
0.183
|
52.16
|
66.60
|
-0.00
|
0.46
|
0.491
|
-117.88
|
64.11
|
0.00
|
0.35
|
0.140
|
-7.76
|
32.17
|
0.00
|
0.43
|
0.825
|
|
FeOx
|
56.26
|
56.00
|
0.50
|
0.50
|
0.389
|
5.62
|
29.96
|
0.14
|
0.74
|
0.860
|
53.61
|
62.94
|
0.44
|
0.51
|
0.442
|
-109.38
|
118.45
|
-0.50
|
0.54
|
0.424
|
65.38
|
59.85
|
0.65
|
0.60
|
0.336
|
76.47
|
97.59
|
0.44
|
0.56
|
0.490
|
|
E
|
-4.06
|
56.52
|
-0.04
|
0.50
|
0.947
|
16.95
|
45.47
|
0.28
|
0.74
|
0.728
|
-16.47
|
27.10
|
-0.31
|
0.51
|
0.576
|
-28.37
|
46.69
|
-0.33
|
0.54
|
0.586
|
114.49
|
68.63
|
0.99
|
0.60
|
0.171
|
-36.82
|
29.46
|
-0.70
|
0.56
|
0.300
|
|
Observations
|
6
|
7
|
7
|
6
|
7
|
6
|
|
R2 / R2 adjusted
|
0.252 / -0.247
|
0.157 / -0.265
|
0.480 / 0.220
|
0.239 / -0.268
|
0.418 / 0.127
|
0.344 / -0.094
|


#Figure for each site where the plots are in order of MAOM N concentration and the top three genera in each plot are labels on the dots
ggplot(final_data_tree_spp, aes(x=E, y=mg.N.per.g.soil_HF))+
geom_point(color="blue")+
geom_smooth(method="lm", color="blue")+
geom_label_repel(aes(label=genus))+
facet_wrap(~Site)+
theme_cowplot()
## `geom_smooth()` using formula 'y ~ x'
## Warning: ggrepel: 18 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 15 unlabeled data points (too many overlaps). Consider increasing max.overlaps
## ggrepel: 15 unlabeled data points (too many overlaps). Consider increasing max.overlaps
## Warning: ggrepel: 11 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 17 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 9 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 21 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

ggsave("top_species_MAOM_N_conc.jpg", path="figures/", width= 40, height= 36, units="cm")
## `geom_smooth()` using formula 'y ~ x'
## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ecm_order=received_samples_plot %>%
select(plotID, order) %>%
distinct()
## Adding missing grouping variables: `Site`
pctAngio=ggplot(plotPctAngio %>%
rename("Angiosperm"="fractionAngiosperm") %>%
mutate(Gymnosperm=1-Angiosperm,
plotID=as.character(plotID))%>%
pivot_longer(c(Angiosperm, Gymnosperm), names_to="gymAng", values_to="dominance") %>%
left_join(ecm_order) , aes(x=order, y=dominance, fill=gymAng))+
geom_bar(position="stack", stat="identity", colour="black")+
scale_fill_manual( values=c("gray90", "gray20"))+
xlab("Study Plot")+
ylab("Proportion of Basal Area")+
facet_wrap(~Site, scales="free_x")+
theme_cowplot()+
theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(),strip.background = element_rect( fill="white"),legend.title=element_blank())
## Joining, by = "plotID"
ggsave("pctAngioGymno.jpg", path="figures/", width= 20, height= 16, units="cm")
BAbyMYC=ggplot(final_data %>%
left_join(ecm_order), aes(x=order, y=plotBA))+
geom_bar( stat="identity", colour="black")+
xlab("Study plots in order of increasing ECM dominance")+
ylab("Basal Area")+
facet_wrap(~Site, scales="free_x")+
theme_cowplot()+
theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(),strip.background = element_rect( fill="white"),legend.title=element_blank())
## Joining, by = c("plotID", "Site")
ggsave("BAmyc.jpg", path="figures/", width= 20, height= 16, units="cm")
ggplot(final_data, aes(x=fractionAngiosperm, y=proportion.C_HF, colour=Site))+
geom_smooth(method="lm",size=2, se=F)+
geom_point(size=3, alpha=0.5)+
labs(x=" Angiosperm dominance ", y="MAOM C (proportion)", colour="Site")+
theme_cowplot()+
theme(legend.title=element_text(hjust=0.5, size=16), legend.text=element_text(size=15), axis.text=element_text(size=15), axis.title=element_text(size=19))
## `geom_smooth()` using formula 'y ~ x'

ggsave("maom.ang.prop.jpg", path="figures/", width= 19, height= 16, units="cm")
## `geom_smooth()` using formula 'y ~ x'
ggplot(final_data, aes(x=fractionAngiosperm, y=mg.C.per.g.soil_HF, colour=Site))+
geom_smooth(method="lm",size=2, se=F)+
geom_point(size=3, alpha=0.5)+
labs(x=" Angiosperm dominance ", y="[MAOM C] (mg/g soil)", colour="Site")+
theme_cowplot()+
theme(legend.title=element_text(hjust=0.5, size=16), legend.text=element_text(size=15), axis.text=element_text(size=15), axis.title=element_text(size=19))
## `geom_smooth()` using formula 'y ~ x'

ggsave("maom.ang.conc.jpg", path="figures/", width= 19, height= 16, units="cm")
## `geom_smooth()` using formula 'y ~ x'
|
|
mg.C.per.g.soil_HF
|
mg.N.per.g.soil_HF
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
|
(Intercept)
|
15.428
|
3.499
|
0.011
|
0.283
|
<0.001
|
1.251
|
0.188
|
0.002
|
0.206
|
<0.001
|
|
plotBA
|
0.542
|
1.342
|
0.055
|
0.137
|
0.688
|
-0.043
|
0.088
|
-0.076
|
0.155
|
0.628
|
|
Random Effects
|
|
σ2
|
52.139
|
0.255
|
|
τ00
|
40.407 Site
|
0.046 Site
|
|
ICC
|
0.437
|
0.154
|
|
N
|
7 Site
|
7 Site
|
|
Observations
|
46
|
46
|
|
Marginal R2 / Conditional R2
|
0.003 / 0.438
|
0.005 / 0.159
|
#Relationships between site-level climate decomposition index (CDI) and a) soil oxalate-extractable iron content, and b) total tree basal area in our study plots.
basal_area_cdi=ggplot(final_data, aes(x=CDI, y=plotBA))+
geom_point()+
stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
theme_cowplot()+
xlab("Climate Decomposition Index")+
ylab("Total Basal Area"
)
feox_cdi=ggplot(final_data, aes(x=CDI, y=FeOx))+
geom_point()+
stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
geom_smooth(method="lm")+
theme_cowplot()+
xlab("Climate Decomposition Index")+
ylab("Soil Oxalate-Extractable Iron"
)
ggarrange(basal_area_cdi, feox_cdi, ncol=2, nrow=1, labels= c("a","b"), legend=F)
## `geom_smooth()` using formula 'y ~ x'

ggsave("FigS2.pdf", path="figures/", width= 24, height= 12, units="cm")
summary(lm(FeOx~CDI,data=final_data )) #yes, more FeOx in warmer sites
##
## Call:
## lm(formula = FeOx ~ CDI, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.54369 -0.29261 0.01529 0.14315 1.21466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0886 0.1845 -0.480 0.63335
## CDI 17.6747 5.1182 3.453 0.00124 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3719 on 44 degrees of freedom
## Multiple R-squared: 0.2132, Adjusted R-squared: 0.1954
## F-statistic: 11.93 on 1 and 44 DF, p-value: 0.001236
summary(lm(mg.C.per.g.soil_HF~FeOx,data=final_data %>% filter(Site=="LENO") )) #yes within this site Fe Ox is a big driver of concentrations
##
## Call:
## lm(formula = mg.C.per.g.soil_HF ~ FeOx, data = final_data %>%
## filter(Site == "LENO"))
##
## Residuals:
## 1 2 3 4 5 6 7
## -4.1075 4.7009 -1.6658 2.2706 -1.6290 0.2162 0.2147
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.594 2.559 1.405 0.21905
## FeOx 9.070 2.148 4.222 0.00831 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.151 on 5 degrees of freedom
## Multiple R-squared: 0.781, Adjusted R-squared: 0.7372
## F-statistic: 17.83 on 1 and 5 DF, p-value: 0.008308
summary(lm(mg.C.per.g.soil_HF~FeOx,data=final_data %>% filter(Site=="DELA") )) #yes within this site Fe Ox is a big driver of concentrations
##
## Call:
## lm(formula = mg.C.per.g.soil_HF ~ FeOx, data = final_data %>%
## filter(Site == "DELA"))
##
## Residuals:
## 1 2 3 4 5 6
## -1.73747 -0.98391 2.70828 2.51069 0.00396 -2.50155
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.900 3.359 0.566 0.6018
## FeOx 12.713 3.577 3.555 0.0237 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.444 on 4 degrees of freedom
## Multiple R-squared: 0.7595, Adjusted R-squared: 0.6994
## F-statistic: 12.63 on 1 and 4 DF, p-value: 0.0237
summary(lm(FeOx~E,data=final_data )) #no overall trend with FeOx and ECM dominance across sites
##
## Call:
## lm(formula = FeOx ~ E, data = final_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5086 -0.2592 -0.1037 0.1383 1.4144
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4170 0.1120 3.725 0.000553 ***
## E 0.2107 0.1929 1.092 0.280790
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4137 on 44 degrees of freedom
## Multiple R-squared: 0.02638, Adjusted R-squared: 0.004257
## F-statistic: 1.192 on 1 and 44 DF, p-value: 0.2808
summary(lm(FeOx~E,data=final_data %>% filter(Site=="LENO") )) # yes within LENO positive relationship between soil FeOx and ECM dominance
##
## Call:
## lm(formula = FeOx ~ E, data = final_data %>% filter(Site == "LENO"))
##
## Residuals:
## 1 2 3 4 5 6 7
## 0.64909 -0.03906 -0.09622 -0.24020 -0.15363 0.32898 -0.44897
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3968 0.2776 1.429 0.2123
## E 1.1924 0.4197 2.841 0.0362 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4058 on 5 degrees of freedom
## Multiple R-squared: 0.6175, Adjusted R-squared: 0.541
## F-statistic: 8.072 on 1 and 5 DF, p-value: 0.0362
summary(lm(FeOx~E,data=final_data %>% filter(Site=="DELA") )) # not at all within DELA
##
## Call:
## lm(formula = FeOx ~ E, data = final_data %>% filter(Site == "DELA"))
##
## Residuals:
## 1 2 3 4 5 6
## -0.52932 -0.02020 -0.03696 -0.01102 0.24494 0.35256
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.88744 0.21678 4.094 0.0149 *
## E 0.02805 0.50443 0.056 0.9583
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3415 on 4 degrees of freedom
## Multiple R-squared: 0.0007722, Adjusted R-squared: -0.249
## F-statistic: 0.003091 on 1 and 4 DF, p-value: 0.9583
#Site means of MAOM C and N concentrations and proportions, ordered by climate decomposition index
MAOM_C_conc_cdi=ggplot(final_data, aes(x=CDI, y=mg.C.per.g.soil_HF))+
geom_point()+
stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
geom_smooth(method="lm")+
theme_cowplot()+
xlab("Climate Decomposition Index")+
ylab("[MAOM C] (mg/g soil)"
)
MAOM_C_prop_cdi=ggplot(final_data, aes(x=CDI, y=proportion.C_HF))+
geom_point()+
stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
theme_cowplot()+
xlab("Climate Decomposition Index")+
ylab(expression("proportion C"[MAOM])
)
MAOM_N_conc_cdi=ggplot(final_data, aes(x=CDI, y=mg.N.per.g.soil_HF))+
geom_point()+
stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
theme_cowplot()+
xlab("Climate Decomposition Index")+
ylab("[MAOM N] (mg/g soil)"
)
MAOM_N_prop_cdi=ggplot(final_data, aes(x=CDI, y=proportion.N_HF))+
geom_point()+
stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
geom_smooth(method="lm")+
theme_cowplot()+
xlab("Climate Decomposition Index")+
ylab(expression("proportion N"[MAOM])
)
ggarrange(MAOM_C_conc_cdi, MAOM_C_prop_cdi, MAOM_N_conc_cdi, MAOM_N_prop_cdi, ncol=2, nrow=2, labels= c("a","b", "c", "d"), legend=F)
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("FigXXX.jpg", path="figures/", width= 24, height= 22, units="cm")
#Is the amount of oxalate-extractable iron in soil related to mycorrhizal type?
ggplot(final_data, aes(x=E, y=FeOx))+
geom_point(color="blue")+
geom_smooth(method="lm", color="blue")+
facet_wrap(~Site)+
theme_cowplot()
## `geom_smooth()` using formula 'y ~ x'

ggsave("ECM_vs_FeOx.jpg", path="figures/", width= 30, height= 24, units="cm")
## `geom_smooth()` using formula 'y ~ x'
|
|
C.N_FLF
|
C.N_OLF
|
C.N_HF
|
|
Predictors
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
std. p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
std. p
|
Estimates
|
std. Error
|
std. Beta
|
standardized std. Error
|
p
|
std. p
|
|
(Intercept)
|
22.642
|
5.408
|
-0.002
|
0.175
|
<0.001
|
0.991
|
20.437
|
7.337
|
-0.005
|
0.203
|
0.008
|
0.982
|
20.415
|
2.790
|
-0.016
|
0.152
|
<0.001
|
0.916
|
|
E
|
10.123
|
8.052
|
0.410
|
0.135
|
0.216
|
0.004
|
16.195
|
10.041
|
0.494
|
0.130
|
0.115
|
0.001
|
7.990
|
3.473
|
0.167
|
0.082
|
0.027
|
0.049
|
|
CDI
|
93.307
|
144.967
|
0.108
|
0.196
|
0.524
|
0.583
|
192.450
|
197.621
|
0.187
|
0.222
|
0.336
|
0.406
|
-231.952
|
75.550
|
-0.785
|
0.163
|
0.004
|
<0.001
|
|
FeOx
|
-6.124
|
2.635
|
-0.414
|
0.178
|
0.025
|
0.025
|
-3.573
|
3.462
|
-0.188
|
0.182
|
0.308
|
0.308
|
0.668
|
1.243
|
0.064
|
0.119
|
0.594
|
0.594
|
|
E * CDI
|
-65.583
|
224.946
|
-0.037
|
0.127
|
0.772
|
0.772
|
-116.203
|
281.347
|
-0.051
|
0.124
|
0.682
|
0.682
|
-166.559
|
97.547
|
-0.133
|
0.078
|
0.096
|
0.096
|
|
Random Effects
|
|
σ2
|
27.164
|
41.285
|
4.859
|
|
τ00
|
3.844 Site
|
11.499 Site
|
2.249 Site
|
|
ICC
|
0.124
|
0.218
|
0.316
|
|
N
|
7 Site
|
7 Site
|
7 Site
|
|
Observations
|
46
|
46
|
46
|
|
Marginal R2 / Conditional R2
|
0.235 / 0.330
|
0.219 / 0.389
|
0.630 / 0.747
|
`